Model calibration and automated trading agent for Euro futures

Abstract

We explored the application of a machine learning method, Logitboost, to automatically calibrate a trading model using different versions of the same technical analysis indicators. This approach takes advantage of boosting's feature selection capability to select an optimal combination of technical indicators and design a new set of trading rules. We tested this approach with high-frequency data of the Dow Jones EURO STOXX 50 Index Futures (FESX) and the DAX Futures (FDAX) for March 2009. Our method was implemented with different learning algorithms and outperformed a combination of the same group of technical analysis indicators using the parameters typically recommended by practitioners. We incorporated this method of model calibration in a trading agent that relies on a layered structure consisting of the machine learning algorithm described above, an online learning utility, a trading strategy, and a risk management overlay. The online learning layer combines the output of several experts and suggests a short or long position. If the expected position is positive (negative), the trading agent sends a buy (sell) limit order at prices slightly lower (higher) than the bid price at the top of the buy (sell) order book less (plus) transaction costs. If the order is not 100% filled within a fixed period (i.e. 1 minute) of being issued, the existent limit orders are cancelled, and limit orders are reissued according to the new experts' forecast. As part of its risk management capability, the trading agent eliminates any weak trading signal. The trading agent algorithm generated positive returns for the two major European index futures (FESX and FDAX) and outperformed a buy-and-hold strategy.

Keywords:

• Automated trading
• Machine learning
• Algorithmic trading
• Agent based economics
• Trading agents
• Boosting

Acknowledgements

The author thanks Stephanie Hammer, Axel Vischer and the Eurex team in Chicago for providing the data and for discussing initial versions of this research. The author also thanks Ionut Florescu, H. Eugene Stanley, Jeff Nickerson, participants of the Eurex workshop at the University of Chicago, and at the High Frequency data conference at Stevens Institute of Technology for suggestions and informal discussions about the trading algorithm, to OneMarketData, Maxim Chernobayev, Maria Belianina, and Ross Dubin for making OneTick available for this research, and to Patrick Jardine for proof-reading the article. The opinions presented are the exclusive responsibility of the author.

Notes

†Technical analysis or technical trading strategies try to exploit statistically measurable short-term market opportunities, such as trend spotting and momentum, in individual industrial sectors (e.g. financial, pharmaceutical, etc.).

†Intuitively, a weak learner is an algorithm with a performance at least slightly better than random guessing.

‡Mapping x to {0, 1} instead of { − 1, +1} increases the flexibility of the weak learner. Zero can be interpreted as ‘no prediction’.

†For a presentation of the Santa Fe stock market model, see Arthur et al. (1997) and LeBaron et al. (1998), and a later version of LeBaron (2001). LeBaron (2000) also has a general review of papers in the area of agent-based finance.

†See appendix for an explanation.

‡See <http://cran.r-project.org> and <http://www.rmetrics.org> for information about R and Rmetrics, respectively.